# Unraveling the Crystallization Kinetics of the Ge$\_2$Sb$\_2$Te$\_5$ Phase Change Compound with a Machine-Learned Interatomic Potential Source: [Unraveling the Crystallization Kinetics of the Ge$\_2$Sb$\_2$Te$\_5$ Phase Change Compound with a Machine-Learned Interatomic Potential]() Collection index: [llms.txt]() Data updated: 2025-10-25T05:47:56.738Z Authors: Omar Abou El Kheir, Luigi Bonati, Michele Parrinello, Marco Bernasconi Published: 2023-04-06T14:37:50.000Z ## Abstract The phase change compound Ge$\_2$Sb$\_2$Te$\_5$ (GST225) is exploited in advanced non-volatile electronic memories and in neuromorphic devices which both rely on a fast and reversible transition between the crystalline and amorphous phases induced by Joule heating. The crystallization kinetics of GST225 is a key functional feature for the operation of these devices. We report here on the development of a machine-learned interatomic potential for GST225 that allowed us to perform large scale molecular dynamics simulations (over 10000 atoms for over 100 ns) to uncover the details of the crystallization kinetics in a wide range of temperatures of interest for the programming of the devices. The potential is obtained by fitting with a deep neural network (NN) scheme a large quantum-mechanical database generated within Density Functional Theory. The availability of a highly efficient and yet highly accurate NN potential opens the possibility to simulate phase change materials at the length and time scales of the real devices. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()